Probabilistic skyline operator over sliding windows

Zhang, W; Lin, X; Zhang, Y; Wang, W; Zhu, G; Yu, JX

Probabilistic skyline operator over sliding windows

Zhang, W Lin, X Zhang, Y

Wang, W Zhu, G Yu, JX

Permalink

Publication Type:: Journal Article
Citation:: Information Systems, 2013, 38 (8), pp. 1212 - 1233
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	2013005440OK.pdf		1.19 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Zhu, G	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Information Systems, 2013, 38 (8), pp. 1212 - 1233	en_US
dc.identifier.issn	0306-4379	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28914
dc.description.abstract	Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficiently computing the skyline over sliding windows on uncertain data elements against probability thresholds. Firstly, we characterize the properties of elements to be kept in our computation. Then, we show the size of dynamically maintained candidate set and the size of skyline. Novel, efficient techniques are developed to process continuous probabilistic skyline queries over sliding windows. Finally, we extend our techniques to cover the applications where multiple probability thresholds are given, "top-k" skyline data objects are retrieved, or elements have individual life-spans. Our extensive experiments demonstrate that the proposed techniques are very efficient and can handle a high-speed data stream in real time. © 2013 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Information Systems	en_US
dc.relation.isbasedon	10.1016/j.is.2012.03.002	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Probabilistic skyline operator over sliding windows	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	38	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficiently computing the skyline over sliding windows on uncertain data elements against probability thresholds. Firstly, we characterize the properties of elements to be kept in our computation. Then, we show the size of dynamically maintained candidate set and the size of skyline. Novel, efficient techniques are developed to process continuous probabilistic skyline queries over sliding windows. Finally, we extend our techniques to cover the applications where multiple probability thresholds are given, "top-k" skyline data objects are retrieved, or elements have individual life-spans. Our extensive experiments demonstrate that the proposed techniques are very efficient and can handle a high-speed data stream in real time. © 2013 Elsevier Ltd. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28914